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NameSizeModeActions
benchmark/-0755rm
commands/-0755rm
data/-0755rm
models/-0755rm
onnx/-0755rm
pipelines/-0755rm
sagemaker/-0755rm
utils/-0755rm
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training_args.py680420644editdlrm
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__init__.py1719520644editdlrm
Edit: /usr/local/lib/python3.6/site-packages/transformers/pytorch_utils.py (1649B)
# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from packaging import version from torch import _softmax_backward_data from .utils import logging logger = logging.get_logger(__name__) is_torch_less_than_1_8 = version.parse(torch.__version__) < version.parse("1.8.0") is_torch_less_than_1_11 = version.parse(torch.__version__) < version.parse("1.11") def torch_int_div(tensor1, tensor2): """ A function that performs integer division across different versions of PyTorch. """ if is_torch_less_than_1_8: return tensor1 // tensor2 else: return torch.div(tensor1, tensor2, rounding_mode="floor") def softmax_backward_data(parent, grad_output, output, dim, self): """ A function that calls the internal `_softmax_backward_data` PyTorch method and that adjusts the arguments according to the torch version detected. """ if is_torch_less_than_1_11: return _softmax_backward_data(grad_output, output, parent.dim, self) else: return _softmax_backward_data(grad_output, output, parent.dim, self.dtype)